System and method for assessing breast cancer risk using imagery

ABSTRACT

This invention provides a system and method for assessing risk of a breast cancer diagnosis based upon imagery of tissue and (optionally) other patient-related factors. A CAD (or similar) system analyzes the imagery and generates a plurality of numerical feature values. An assessment module receives inputs from patient factors and history and computes the risk based upon the feature values and the patient factors and history. A masking module receives inputs from the patient factors and history, and computes the risk of having a cancer, which cancer is otherwise characterized by a low probability of detection, based upon the feature values and the patient factors and history. A recall module receives inputs from the assessment module and the masking assessment module, and generates a computer-aided indication of a clinical follow-up by the patient. Results of assessment(s) can be displayed to the clinician and/or patient using a graphical interface display.

FIELD OF THE INVENTION

This invention relates to systems and methods to determine risk forbeing diagnosed with a cancerous condition in (e.g.) the breast, basedon imagery of breast tissue and other tissues, and more particularly tosystems and methods that can also incorporate genetic risk variants,inherited risks, personal history, and/or lifestyle factors.

BACKGROUND OF THE INVENTION

Risk prediction models for breast cancer use lifestyle factors, familyhistory of breast cancer, mammographic density, genetic determinants, orany combination of these factors to predict risk of developing thedisease. Mammographic density is one of the strongest risk factors forbreast cancer and consists of the radiographically dense fibroglandularpart of the mammogram. Women with dense breasts have both an increasedrisk of breast cancer and a higher probability for a cancer being masked(undetected). Women with abnormal tissue changes in the breast haveincreased risk for later diagnosis of breast cancer. It is currentlymandatory by law to report the level of mammographic density to a womanundergoing a mammography in 27 U.S. states, but there is no obligationto report the risk of breast cancer.

Current computer-aided detection (CAD) software applications/systems aredesigned to support radiologists at mammographic screening units indiagnosing early breast cancer. These systems and/or type(s) of softwarecan indicate suspicious microcalcifications and masses. One such systemis described in commonly assigned U.S. Pat. No. 8,855,388, entitledMICROCALCIFICATION DETECTION CLASSIFICATION IN RADIOGRAPHIC IMAGES,issued Oct. 2, 2014, the teachings of which are expressly incorporatedherein by reference as useful background information. Briefly, thissystem operates on input digitized (e.g.) mammography image data of abreast, in which the digitized image is repeatedly convolved to formfirst convolved images. The first convolved images are convolved asecond time to form second convolved images. Each first convolved imageand the associated respective second convolved image represent a stage,and each stage represents a different scale or size of anomaly. As anexample, the first convolution may utilize a Gaussian convolver, and thesecond convolution may utilize a Laplacian convolver, but otherconvolvers may be used. After being derived, the second convolved imagefrom a current stage and the first convolved image from a previous stageare then used with a neighborhood median determined from the secondconvolved image from the current stage by a peak detector to detectpeaks, or possible anomalies for that particular scale.

A study by Eriksson et al. (incorporated herein by reference as usefulbackground information) shows that a CAD detection system could be usedto extend risk models by including microcalcifications and masses asrisk factors for later being diagnosed with breast cancer. See, in theattached Appendix, Mikael Eriksson, Kamila Czene, Audi Pawitan, KarinLeifland, Hatef Darabi and Per Hall, entitled A clinical model foridentifying the short-term risk of breast cancer, Breast Cancer Research(2017) 19:29 (a study in association with Karolinska Institute ofStockholm, Sweden). This study compared women at highest mammographicdensity yielded a five-fold higher risk of breast cancer compared towomen at lowest density. When adding suspicious lesions withmicrocalcifications and masses to the model, high-risk women had anearly nine-fold higher risk of breast cancer than those at lowest risk.In the full model, taking HRT use, family history of breast cancer, andmenopausal status into consideration, the area under the curve (AUC)reached 0.71. Notably, this study takes into account breast image datain determining risk. In this case, the image data associated with breastdensity and texture is employed. More particularly, the number ofmicrocalcifications present in the left breast versus right breast istaken into account in the assessment. Images prior to diagnostic imagesare used to determine at-risk lesions in the breast.

There exist other models for determining breast cancer risk—for examplethe Tyrer-Cuzick, BOADICEA, and Gail models. These models do not takebreast imagery for at-risk lesions into consideration. It is desirableto leverage the availability of high-resolution breast image informationto improve the determination of a risk score in breast cancer screening.

SUMMARY OF THE INVENTION

This invention overcomes disadvantages of the prior art by providing aneasily implementable prediction tool for individualized breast cancerscreening without adding substantial cost or effort to the health caresystem, that can be based on detection of structures in the tissue, suchas microcalcifications. Such microcalcifications can be based (e.g.) ondetection of at-risk lesions. The characteristics of these detectedstructures can be optionally combined with risk factors, including, butnot limited to, with mammographic density, genetic risk variants,inherited risks, comorbidities, hormonal and lifestyle factors. Theprocess(or) employed includes a CAD (or similarly configured andfunctioning) computing system, based upon a deep learning, AI, neuralnetwork, and/or similar computing/data-handling arrangement, which canproduce highly accurate features from acquired imagery. Notably, the CADsystem for at-risk lesions is employed to augment and facilitate aprediction of risk of developing a disease (e.g. breast cancer) inaddition to more traditional applications, in which the CAD is employedto diagnose existing conditions. More particularly, the acquired breastimage data can provide additional image features that are, in turn, usedto derive more informative predictive risk score models than previouslyemployed microcalcifications and density (masses) measurements.

In an illustrative embodiment, a system and method for assessing risk ofbeing diagnosed with cancer based upon imagery of tissue and(optionally) other patient-related factors is provided. A CAD (orsimilarly configured) system analyzes the imagery and generates aplurality of numerical feature values. An assessment module (optionally)receives inputs from patient factors and history and computes the riskbased upon the feature values and the patient factors and history. Amasking determination module receives inputs from the patient-relatedfactors, and computes the risk of having a cancer, which cancer isotherwise characterized by a low probability of detection, based uponthe feature values and the patient factors and history. A recalldetermination module receives inputs from the assessment module and themasking assessment module, and generates a computer-aided indication ofa clinical follow-up by the patient. The assessment module can alsoreceive score data from prior imagery of tissue for monitoring a therapyresponse by a clinician. Results of assessment(s) can be displayed tothe clinician and/or patient using a graphical interface display.Illustratively, the assessment module receives score data from priorimagery of tissue that is verified to include cancer by a specialist.The assessment module also receives score data from prior imagery oftissue for monitoring a therapy response by a clinician. Thepatient-related factors can include at least one of (a) breast imagery,(b) percent tissue density, (c) density compactness, (d) age when theimagery is acquired, (e) BMI, (f) menopause status, (g) family historyof cancer, (h) personal history of disease, (i) lifestyle, (j) geneticvariants, and (k) information from prior health care examinations. Also,the assessment module can be arranged to determine cancer of a specificsubtype or generalized breast cancer. The features are established basedupon lesion candidates localized in the tissue by the CAD system.Additionally, the risk assessment module can employ breast sidedifferences for microcalcifications, masses and the tissue densityand/or can employ an interaction between tissue density and masses. Auser interface output module that can provide a graphical display outputof the risk versus a scale of risk values, the masking versus a scale ofmasking values, and/or a recall score. In various embodiments, thetissue is human breast tissue and the imagery is mammography imagery.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention description below refers to the accompanying drawings, ofwhich:

FIG. 1 is a diagram showing the delivery of breast imagery data to aprocessor and associated process for carrying out risk assessment anddisplaying results to a user/clinician in accordance with the system andmethod herein;

FIG. 1A is a flow diagram of an overall runtime procedure for inputtingdata and imagery employing the arrangement of FIG. 1 ;

FIG. 1B is a more detailed flow diagram of the overall runtime procedureof FIG. 1B;

FIG. 2 is a flow diagram showing the generation/training of a calibratedclassifier from patient mammography data for use in runtime riskassessment processes;

FIG. 2A is an exemplary graph showing patient cancer risk at a singletime point versus population prevalence over a range of ages for use inrisk assessment and training in accordance with the system and method;

FIG. 2B is an exemplary graph showing patient cancer risk over timeversus population prevalence over a range of ages for use in riskassessment and training in accordance with the system and method;

FIG. 3 is an exemplary acquired/input image of a breast for runtimeanalysis by the CAD system in accordance with the system and method;

FIG. 3A is an exemplary set of left breast and right breast images forinput to the CAD system in accordance with FIG. 3 ;

FIG. 4 is an exemplary image of the breast according to FIG. 3 showingthe identification and location of candidate lesions in the tissue forfurther analysis by the CAD system;

FIG. 5 is an exemplary image of the breast according to FIG. 3 showingthe computation of feature values based on the identification andlocation of candidate lesions by the CAD system according to FIG. 4 ;and

FIGS. 6-8 are exemplary displays presented to a user by the system andmethod showing risk and recall scores graphically and as a percentagevalue.

DETAILED DESCRIPTION I. System Overview

Reference is made to FIG. 1 , which shows a generalized arrangement 100in which image data 110 is derived from an imaging device 112 or storeof acquired imaged—for example those acquired from a scan of a patient114 based upon (e.g.) mammography. The image data 110 is provided to aprocessor and associated analysis process 120. The process(or) 120 ispart of a deep learning computer-aided detection (CAD) system 130 thatbuilds and/or employs neural networks, or similar learningarrangements/data structures (e.g. AI-based systems) to derive scoresfor use in evaluating the significance of detected structures within theimage and underlying tissue. Note, that as used herein, the term “CAD”can refer to a computing system (hardware, software and/or firmware)that performs computer-aided detection processes, and can also(optionally) perform computer-aided analysis processes in a manner clearto those of skill. In addition, the term “CAD” can be taken to includecomputing systems that have been optimized for risk prediction in amanner clear to those of skill and/or in a customized manner. Theprocess(or) can be instantiated on any acceptable computing device orenvironment (e.g. a laptop, PC, server, cloud, etc.) as exemplified bythe device 140, which includes a display and/or touchscreen 142,keyboard 144 and mouse 146.

The process(or) 120 is provided with various factors 150, some of whichare input by the user via the device 140 using an appropriate interface.These factors can include, significantly, breast/tissue density. Suchdensity and related data can be derived from mammography delivered viathe imaging device 112 or via another source, including prior, or modelmammography data 152. Other data can be based upon age, previous historyof cancer, lifestyle, including (e.g.) alcohol consumption, diet,smoking, drug use, sleep patterns, familial genetics, polygenic factors,etc. The processor 120 also determines the existence in the image dataof (e.g.) microcalcifications. These elements are applied to a riskassessment process(or) 124 that employs a CAD score that is part of thedeep learning computations.

FIG. 1A shows a generalized runtime procedure 160 performed by, and inassociation with, the arrangement 100 of FIG. 1 . FIG. 1B shows agraphical representation 170 of the procedure 160. As shown, in step162, the procedure 160 inputs one or more types of mammography imagery172 from the patient; for example, 2D FFDM images, 3D tomosynthesisvolumes, and/or 2D synthetic images. The system uses this image data tocompute a range of CAD features (numerical values as described below)174. The system (for example, via interface 152) can input optional data176 related to the patient, including, but not limited to, (a) the ageof patient, (b) lifestyle familial factors, (c) polygenic (DNA based)risk factors/scores (e.g. as described in the above-referencedKarolinska study), and/or (d) breast density.

In step 164 of the overall procedure 160, the system and method hereincomputes accurate short term, and long-term risks scores 178 (as alsodescribed further below) using the input information from step 162.Then, in step 166, the overall procedure 160 generates and displays (andoptionally stores), on (e.g.) an interface device 140, the results withassociated short term and long term risk scores to the end user (e.g. aclinician, patient, etc.) based upon the computations in step 164. Thedisplay can embody a variety of formats and presentation styles—e.g.meters, bar graphs, curves and/or color-coded fields. For example, therisk score “meter” display 180 provides both a graphical and percentagedisplay of a 2-year risk (between “low”, “average”, and “high”) ofdeveloping a malignancy. This display 180 also includes a listed“masking score”, which is described further below.

II. Score and Classifier Generation

The CAD-generated score, more particularly is employed with thefollowing considerations:

-   -   Breast Density plays a major factor in patient risk    -   Breast Density and CAD features can be derived from mammograms    -   CAD features take into account a plurality of image aspects such        as:        -   of suspicious findings (lesions) in the patient        -   How likely these lesions are masses or microcalcifications        -   How likely the masses or microcalcifications are malignant        -   Texture of the breast        -   Intensity distribution    -   CAD features used in detection of cancers can be employed as        input features for RISK prediction (process(or) 124)    -   These CAD features are derived from training a CAD system to        detect malignant lesions on pre-diagnostic or diagnostic        mammograms.    -   CAD features, along patient information such as the following        are used in the prediction of the Risk score:        -   factors        -   Family history, and age of onset, of breast cancer        -   Genetic variants, e.g. a polygenic risk score        -   Age        -   Body mass index        -   Menopause status        -   Previous history of breast tissue abnormalities        -   Information from prior health care examinations        -   Information on use of alcohol and tobacco

CAD features, along patient information (factors 150, described above)such as the following are used in the prediction of the Risk score inaccordance with the system and method herein.

Reference is now made to the procedure 200 of FIG. 2 , in which theabove information is used by the risk assessment module/process 134 toderive a risk score. In step 210, a set of mammograms and their priorsare provided for the patient. The data includes knowledge as to whetherthe current mammogram contains cancer. In step 220 prior mammograms withknown features including CAD features computed on the images are alsoprovided to the procedure 200. In step 230, and based upon the presentand prior mammogram data, the procedure 200 designs/generates aclassifier to predict if the current mammogram of the same patientcontains cancer or not. In step 240, the classifier score is thencalibrated based on a population prevalence to derive a risk score.

With reference now to FIGS. 2A and 2B, respective graphs 260 and 270 areprovided, showing a plot 262 and 272 the age-related risk for a patientof developing breast cancer versus a general population's risk at agiven age range. The curves for population prevalence in these examplesrepresent a risk of 10-percent (264, 274), average (266, 276) and90-percent (268, 278). The plot 272 of patient risk is extended over 20years in graph 270.

By way of further background, CAD is designed to detect malignantlesions, such as microcalcifications and masses occurring in (e.g.)breast tissue. In the present system and method, the features computedas part of this process take into consideration (a) the number ofsuspicious findings (lesions) in the patient, (b) how likely theselesions are masses or microcalcifications, (c) how likely the masses andmicrocalcifications are malignant, (d) the relative distribution in theleft and right breast, (e) the relative texture of the breast tissue and(f) the relative intensity distribution within the acquired image. Thesefeatures are mapped to respective numerical values for each detectionevent. In one example, the CAD features can be characterized byapproximately 65 floating-point values. Hence, by providing a largenumber of variable features, the overall accuracy of the computed shortterm and long term risk model can be substantially improved over priortechniques.

With reference to FIGS. 3-5 , the process for computing feature valuesis depicted in view of an input image 300 (FIG. 3 ) of a breast. In anexemplary embodiment, shown in FIG. 3A, the imagery can include bothcraniocaudal (CC) 320 and 322 and mediolateral oblique (MLO) 330 and 332views of respective left and right breasts. The CAD system analyzes(block 420) the image(s) 300 using commercially available/knowntechniques, and generates a list with locations 410 of potentialmalignant lesions. After locating potential lesions 410, the CAD systemthen generates feature numerical values 520 for each of these respectivelesions (block 510 in FIG. 5 ), again, using available and knowntechniques.

Note that in step 250, the calibrated classifier can be provided to afollow-on runtime process for use with a new patient. As such,mammography images related to the new patient are input to the riskassessment process, with associated patient information/factors. Thisinformation is then used to output the predicted risk score.

III. Results Presentation

The process(or) 120 (FIG. 1 ) includes a user interface and/or GUIdriving component 126. This is used to generate displays for use by theclinician, or others to visualize the risk computed from the informationprovided. The results of the above-described procedures and computationscan, thus, be displayed to a user and/or clinician for use in advisingpatients and guiding follow-up visits and treatment. Some examples ofexemplary displays are shown in FIGS. 6-8 . In the display 600 of FIG. 6, a 2-year risk score for a patient (e.g. 18 percent of developingbreast cancer) is shown using a pointer 620 associated with a vertical(e.g. color or intensity-coded) bar graph 630. The exemplary display 700in FIG. 7 provides a pointer 720 for 2-year risk score (e.g. 28%) andalso a 2-year recall score pointer 730 (e.g. 18%). In the display 800,the recall score is provided as a pointer 820, while the 2-year recallscore 830 is listed alphanumerically only.

Note that the actual depicted risk value(s) and range can be highlyvariable in exemplary implementations. For example, instead of a 0-100scale, the overall scale between approximately 0% and 2% (or anotherrelatively low, maximum percentage). In such an example, the depicted“LOW” risk value can end at approximately 0.15%; the “AVERAGE” risk canend at approximately 0.6%; the depicted “HIGH” risk can end atapproximately 1.6%; and the depicted “HIGH+” (very high) risk can extendabove 1.6% to a maximum of 2, or more, percent. The overall scale, andvalues selected for each level, can be computed based upon the variousfactors described herein. There can be predetermined thresholds thatsuch computations use to scale the risk levels.

It is contemplated that the risk assessment process 124 can compute amasking score, which is also depicted alphanumerically (640 and 840) inexemplary displays 600 and 800, and with a pointer 740 in display 700.The masking score calculates a particular metric that quantifies risk insituations where the patient carries a high probability for a tumor anda low probability of that tumor being detected within the currentexamination. The masking score is trained on breast cancer cases usingthe risk score determinants and additionally on mode-of-detection(interval cancer vs. screen-detected cancers). The score identifiesspecific image features and patient characteristics that differ betweeninterval and screen-detected cancers. A predicted high masking scoretypically translates into a high probability for interval cancers. Moregenerally, the system's risk assessment module can receive score datafrom prior imagery of the patient's tissue to allow monitoring of atherapy response by a clinician.

Note that it is desirable for the clinician to monitor patients atpredetermined intervals for any changes in risk score and masking scoredue to their response to therapy. In this manner, the clinician can thensuggest any appropriate changes in the individualized program for thatpatient.

The risk assessment process 124 can compute a “recall” score, which isalso depicted alphanumerically (630 and 820) in exemplary displays 600and 800, and with a pointer 730 in display 700. The recall score is acomputer-aided indicator for recommending an individualized healthcareprogram to the patient.

By way of example, reference is made to the UK NICE guidelines(https://www.nice.org.uk/guidance/cg164). An extract of the NICEguidelines health care program for (e.g.) women at high risk of breastcancer is listed below:

-   -   1.6.2 Do not routinely offer ultrasound surveillance to women at        moderate or high risk of breast cancer but consider it:        -   when MRI surveillance would normally be offered but is not            suitable (for example, because of claustrophobia)        -   when results of mammography or MRI are difficult to            interpret.    -   1.6.3. Offer annual mammographic surveillance to women:        -   aged 40-49 years at moderate risk of breast cancer        -   aged 40-59 years at high risk of breast cancer but with a            30% or lower probability of being a BRCA or TP53 carrier        -   aged 40-59 years who have not had genetic testing but have a            greater than 30% probability of being a BRCA carrier        -   aged 40-69 years with a known BRCA1 or BRCA2 mutation.            [2013]

REF

-   https://www.nice.org.uk/guidance/cg164/chapter/Recommendations#surveillance-and-strategies-for-early-detection-of-breast-cancer.

The characterization of the recall score is based upon the risk scoreand the masking score. A higher risk-score indicates that the patient isat higher risk for being diagnosed with breast cancer. A higher maskingscore indicates that the patient is at higher risk, and that the resultsof the modality (e.g. digital mammography) are difficult to interpret. Ahigher recall score further indicates that the patient is at higher riskand/or have a higher masking probability. The recall score is astatistical construct upon the risk and masking scores. The range of therecall score is defined between 0% and 100% and cut-offs on the scaledefines categories. The categories are used to indicate a recommendationfor an individualized health care program to the woman. Examples ofrecommendations are following up current exam using a modality withincreased sensitivity, follow-up for suspicion of cancer, participatingin a program to decrease breast cancer risk or masking, more intensescreening or less frequent screening.

IV. Performance

The following table shows at least a 7% improvement in AUC for the curveof sensitivity versus specificity with the system and method (Model 1),which incorporates CAD detection features into the risk model profferedin the Karolinska study. Other Models further improving the accuracy andtraditional risk models are also depicted by way of comparison.

Discrimination performance AUC (95% CI)¹ Recall score² Risk Maskingeffect Model 1. Mammographic density, microcalcifications, 0.77(0.74-0.80) 0.72 (0.71-0.75)  0.80 (0.78-, 0.83) masses, age 2. Model1 + lifestyle and familial risk factors³ 0.77 (0.74-0.80) 0.73(0.71-0.76) 0.80 (0.78-0.83) 3. Model 2 + PRS 0.79 (0.76-0.82) 0.75(0.72-0.78) 0.80 (0.78-0.83) Comparison to other risk models: PRS (BCAC)0.65 (0.62-0.67) Tyrer-Cuzick 0.63 (0.61-0.65) Gail 0.55 (0.53-0.57)¹Discrimination performance using area under the receiver-operatingcurve (AUC) and 95% confidence intervals. ²Recall score contrastedincident breast cancer cases with controls using the risk score andmasking score as predictors. The risk and masking scores were predictedon the full cohort with time frames from the risk and masking projectionrespectively. ³The included lifestyle and familial risk factors wereBMI, menopause status, current use of HRT, tobacco, alcohol, and familyhistory of breast cancer.

The Hosmer-Lemeshow model fit test statistic for recall models 1, 2, and3 were 0.22, 0.16, and 0.24.

V. Conclusion

It should be clear that the above-described system and method formodelling risk based upon CAD analysis of image data in combination withpatient information and traditional factors (breast density) achieves asubstantial improvement in the accuracy of the results. This approachallows for learning to improve the overall model through progressiveupdate of existing image data through a CAD-based (e.g. deep learning,neural network, AI) computational environment. The results can bedisplayed in a variety of graphical formats that increase the ease ofunderstanding for users and patients.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments of the apparatus and method of the presentinvention, what has been described herein is merely illustrative of theapplication of the principles of the present invention. For example, asused herein, the terms “process” and/or “processor” should be takenbroadly to include a variety of electronic hardware and/or softwarebased functions and components (and can alternatively be termedfunctional “modules” or “elements”). Moreover, a depicted process orprocessor can be combined with other processes and/or processors ordivided into various sub-processes or processors. Such sub-processesand/or sub-processors can be variously combined according to embodimentsherein. Likewise, it is expressly contemplated that any function,process and/or processor herein can be implemented using electronichardware, software consisting of a non-transitory computer-readablemedium of program instructions, or a combination of hardware andsoftware. Additionally, as used herein various directional anddispositional terms such as “vertical”, “horizontal”, “up”, “down”,“bottom”, “top”, “side”, “front”, “rear”, “left”, “right”, and the like,are used only as relative conventions and not as absolutedirections/dispositions with respect to a fixed coordinate space, suchas the acting direction of gravity. Additionally, where the term“substantially” or “approximately” is employed with respect to a givenmeasurement, value or characteristic, it refers to a quantity that iswithin a normal operating range to achieve desired results, but thatincludes some variability due to inherent inaccuracy and error withinthe allowed tolerances of the system (e.g. 1-5 percent). Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

What is claimed is:
 1. A system for assessing risk of being diagnosedwith cancer based upon imagery of tissue and patient-related factorscomprising: a computer-aided detection (CAD) or AI-based system thatanalyzes the imagery and generates a plurality of numerical featurevalues; an assessment module that receives inputs and computes the riskof cancer, which cancer is otherwise characterized by a low probabilityof detection, based upon the feature values; and an output module thatprovides an output of the risk score versus a scale of risk scores. 2.The system as set forth in claim 1 wherein the assessment modulereceives score data from prior imagery of tissue for monitoring atherapy response by a clinician.
 3. (canceled)
 4. The system as setforth in claim 1 wherein the assessment module receives score data fromprior imagery of tissue for monitoring a therapy response by aclinician.
 5. The system as set forth in claim 1, further comprising amasking determination module that receives inputs from the patientfactors and history, and computes the risk of having a cancer, whichcancer is otherwise characterized by a low probability of detection,based upon the feature values and the patient factors and history. 6.The system as set forth in claim 5 wherein the assessment modulereceives score data from prior imagery of tissue for monitoring atherapy response by a clinician.
 7. The system as set forth in claim 5,further comprising a recall determination module that receives inputsfrom the assessment module and the masking assessment module, andgenerates a computer-aided indication of a clinical follow-up by thepatient.
 8. The system as set forth in claim 1 wherein the assessmentmodule receives score data from prior imagery of tissue that is verifiedto include cancer by a specialist.
 9. The system as set forth in claim 1wherein the patient-related factors include at least one of (a) breastimagery, (b) percent tissue density, (c) density compactness, (d) agewhen the imagery is acquired, (e) BMI, (f) menopause status, (g) familyhistory of cancer, (h) personal history of disease, (i) lifestylefactors, (j) genetic variants, and (k) information from prior healthcare examinations.
 10. The system as set forth in claim 1 wherein theassessment module determines cancer of a specific subtype or generalizedbreast cancer.
 11. The system as set forth in claim 10 wherein thefeatures are established based upon lesion candidates localized in thetissue by the CAD system.
 12. The system as set forth in claim 11,wherein the risk assessment module employs differences between eachbreast side for microcalcifications, masses and the tissue density. 13.The system as set forth in claim 12 wherein the risk assessment moduleemploys an interaction between tissue density and masses.
 14. The systemas set forth in claim 1 further comprising a user interface outputmodule that provides a graphical display output of the risk versus ascale of risk values.
 15. The system as set forth in claim 5, furthercomprising a user interface output module that provides a graphicaldisplay output of the masking versus a scale of masking values.
 16. Thesystem a set forth in claim 15 wherein the display output provides arecall score.
 17. The system as set forth in claim 1 wherein the tissueis human breast tissue and the imagery is mammography imagery.
 18. Amethod for assessing risk of being diagnosed with cancer based uponimagery of tissue and other patient-related factors, comprising thesteps of: analyzing, with a computer-aided detection (CAD) or AI-basedsystem, imagery and generating a plurality of numerical feature values;receiving inputs from the CAD system with the feature values, andreceiving the patient-related factors; computing the risk based upon thefeature values and the patient-related factors; determining a maskingscore, including receiving inputs from the patient factors and history;and computing the risk of having a cancer, which cancer is otherwisecharacterized by a low probability of detection, based upon the featurevalues and the patient factors and history. 19-20. (canceled)
 21. Themethod as set forth in claim 18, further comprising determining a recallscore, including receiving inputs from the assessment module and themasking assessment module, and generating a computer-aided indication ofa clinical follow-up by the patient.
 22. The system as set forth inclaim 18, wherein the step of determining a masking score includesreceiving inputs from the patient factors and history, and computing therisk of having a cancer, which cancer is otherwise characterized by alow probability of detection, based upon the feature values and thepatient factors and history.
 23. The method as set forth in claim 1,further comprising, a masking determination module that receives inputsfrom the patient factors and history, and computes the risk of having acancer, which cancer is otherwise characterized by a low probability ofdetection, based upon the feature values and the patient factors andhistory.